(146c) Magnetic Resonance Imaging of Wet Fluidization

In the energy, pharmaceuticals and polymer production industries, it is common for small amounts of liquid to be injected into gas-solid fluidized beds. In some cases, the liquid is a reactant, while in others it facilitates agglomeration or heat transfer. The liquid leaves to the formation of cohesive liquid bridges between particles as well as between particles and walls, often having significant effects on hydrodynamics. These effects include formation of agglomerates and in certain cases can lead to local or device-scale defluidization.

Despite the industrial importance of liquid bridging on gas-solid fluidization, the effect of liquid on fluidization hydrodynamics is still not well understood. A small number of previous experimental studies have demonstrated effects of liquid on bed on bed fluidity^1,2, particle velocities^3–6, bed height¹, bubble size^6,7 and minimum fluidization velocity⁶. Additionally, various computational models have been developed and utilized to understand the effects of liquid bridging on fluidization hydrodynamics. Some studies have simply lowered the coefficient of restitution^8,9 to account for the effects of liquid bridging, while others have modeled the capillary forces in the normal direction^10,11, the viscous forces in the normal and tangential directions¹², as well as the rate of liquid transfer between the surfaces of particles and liquid bridges¹³. Due to a lack of detailed experimental studies of a variety of hydrodynamic features under a variety of liquid conditions, the current literature does not fully elucidate the relative importance of liquid loading, surface tension and viscosity on fluidization behavior. This absence in the experimental literature leads to issues in understanding the validity and areas for improvement in existing computational models.

Previously, MRI has been used to study hydrodynamics in detail in gas-solid fluidized beds. Here, we use MRI to image local particle concentration and velocity with high temporal resolution in a 3D freely bubbling fluidized bed. We use these measurements to characterize both average values and variations in bubble size, bubble number density and particle speed under varying gas flow rates, liquid loading conditions, and values of surface tension and viscosity. We use these results to evaluate the importance and validity of different computational sub-models for liquid bridging.

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